golemverse - {shinipsum} is now on CRAN

I’m very happy to announce that {shinipsum} is now on CRAN!

{shinipsum} is a package that can help you build {shiny} prototypes faster by providing a series of functions that can generate random elements to populate your UI. If you are familiar with “lorem ipsum”, the fake text generator that is used in software design as a placeholder for text, the idea is the same: generating placeholders for Shiny outputs.

{shinipsum} can be installed from CRAN with:

install.packages("shinipsum")

You can install this package from GitHub with:

remotes::install_github("Thinkr-open/shinipsum")

In this package, a series of functions that generates random placeholders. For example, random_ggplot() generates random {ggplot2} elements:

library(shinipsum)
library(ggplot2)

random_ggplot() +
  labs(title = "Random plot")

random_ggplot() +
  labs(title = "Random plot")

Of course, the idea is to combine this with a Shiny interface, for example random_ggplot() will be used with a renderPlot() and plotOutput(). And as we want to prototype but still be close to what your final application will look like, these functions take arguments that can shape the output: for example, random_ggplot() has a type parameter that can help you select a specific geom.

library(shiny)
library(shinipsum)
library(DT)
ui <- fluidPage(
  h2("A Random DT"),
  DTOutput("data_table"),
  h2("A Random Plot"),
  plotOutput("plot"),
  h2("A Random Text"),
  tableOutput("text")
)

server <- function(input, output, session) {
  output$data_table <- DT::renderDT({
    random_DT(5, 5)
  })
  output$plot <- renderPlot({
    random_ggplot(type = "point")
  })
  output$text <- renderText({
    random_text(nwords = 50)
  })
}
shinyApp(ui, server)

Other {shinipsum} functions include:

tables:

random_table(nrow = 3, ncol = 10)

  Plant   Type  Treatment conc uptake Plant.1 Type.1 Treatment.1 conc.1
1   Qn1 Quebec nonchilled   95   16.0     Qn1 Quebec  nonchilled     95
2   Qn1 Quebec nonchilled  175   30.4     Qn1 Quebec  nonchilled    175
3   Qn1 Quebec nonchilled  250   34.8     Qn1 Quebec  nonchilled    250
  uptake.1
1     16.0
2     30.4
3     34.8

print outputs:

random_print(type = "model")


    Pearson's product-moment correlation

data:  datasets::mtcars$mpg and datasets::mtcars$cyl
t = -8.9197, df = 30, p-value = 6.113e-10
alternative hypothesis: true correlation is not equal to 0
95 percent confidence interval:
 -0.9257694 -0.7163171
sample estimates:
      cor 
-0.852162

… and text, image, ggplotly, dygraph, and DT.

Learn more about {shinipsum}: